In automated clinical diagnostics, a droplet of some fluid to be tested is placed on a reactive medium. The resulting reaction produces information relevant to the clinical test being performed. This information is obtained automatically by measuring qualities related to the optical density of the test medium after the reaction has taken place or while the reaction is occurring. However, since the droplet does not spread uniformly, the density pattern on the substrate is not uniform, nor is the droplet placed in precisely the same spot on each measurement. Thus, it is desirable to locate the center of the density pattern, as this corresponds to the position at which the droplet is placed. All diagnostic measurements can then be made relative to this position. This will reduce the variability of the measurements and improve their quality.
Typically, the density patterns are not precisely circular, there is noise in the measurement of the pattern, and the nature of the pattern will be different for different diagnostic procedures. In general, density patterns tend to be a collection of digital values related to the optical densities at specific locations in the original image.
Techniques are known for estimating the center and radius of a circular arc in a binary image, and for finding the centers and radii of multiple such arcs using; for example, the widely known Hough transform. Unfortunately, this requires applying thresholds to the gradient images in order to create binary images and loses information about the strength of the gradient and its local direction. In addition, these techniques find a center for each circular arc, whereas what is often needed is the best overall center for the entire pattern.
It is seen then that it would be desirable to have a modified system and method for determining the best central location in an image.